Copyright by Maxim Victorovich Baryshevtsev 2020 The Dissertation Committee for Maxim Victorovich Baryshevtsev Certifies that this is the approved version of the following dissertation: Sharing is Not Caring: News Features Predict False News Detection and Diffusion Committee: Matthew S. McGlone, Supervisor René M. Dailey Jeffrey Hancock Anita L. Vangelisti Sharing is Not Caring: News Features Predict False News Detection and Diffusion by Maxim Victorovich Baryshevtsev Dissertation Presented to the Faculty of the Graduate School of The University of Texas at Austin in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy The University of Texas at Austin May 2020 Dedication I would like to dedicate this project to my late grandfather Stanislav Pismenny (1940 – 2020). He obtained multiple degrees and certificates in engineering, which helped him build his own house, where my grandmother still lives. He was always a man of reason and innovation, teaching me to attack problems head-on and with an open mind. Without the skills and smarts that he had passed down to me, I doubt I would have been able to complete this difficult journey. Thank you Stas, you will always be in our hearts and minds, pushing us to be better humans every day. Мы тебя любим. Acknowledgements First and foremost, I would like to thank my advisor Dr. Matthew McGlone for his guidance and support throughout my studies. He pushed me to be a better scholar, whether it was through holding me to the highest methodological standards, or always being there to bounce ideas off of. His advice will follow me for the rest of my life. I would also like to thank my parents and sisters for always acting excited to hear about my research, even if it made no sense to them. They all taught me valuable lessons on life and its challenges. Thank you to my animals, Kumar, Sarabi, Astro, Sasha, and Lola, for distracting me at the right times (and sometimes the worst times). And last, but certainly not least, I would like to thank my wife Stephanie for being right there next to me the entire time, cheering me on and supporting me in the most difficult moments. She truly is a superhero. v Abstract Sharing is Not Caring: News Features Predict False News Detection and Diffusion Maxim Victorovich Baryshevtsev, PhD The University of Texas at Austin, 2020 Supervisor: Matthew S. McGlone Misinformation research has identified numerous news story features that predict susceptibility to false news. Four of these features seem to be consistently studied and reported as problematic: belief congruence (false news that matches one’s personal beliefs about the world), political congruence (false news that matches one’s political orientation), moral-emotional language (words that share a sense of morality while being emotionally charged), and social consensus (knowing that others also believe the false news). Reported are two different paradigms where participants were asked to read through a Facebook newsfeed and either choose which posts they would share (diffusion paradigm) or choose which posts were false news (detection paradigm). First, the studies reported below were concerned with determining the effect each of these features had on the detection and diffusion of false news, while accounting for the effects of the other features. Second, the detection paradigm was also used to determine the effect base-rates had on false news detection because according to Truth-Default Theory the number of deceptive messages people encounter in the world is directly related to how accurate they vi will be. All of the news features were found to have at least some effect on the diffusion and detection of false news, with belief congruence (ORdiffusion = 2.8, ORdetection = 1.4) and political congruence (ORdiffusion = 2.4, ORdetection = 1.3) having the strongest and most consistent effects. Regarding testing the effect of base-rates on detection accuracy, the more false news participants encountered, the more accurate they were, indicating the presence of a lie-bias. This contradicts the truth-bias prediction Truth-Default Theory makes, some of which is accounted for by the general suspicion people have of online news. Theoretical and practical implications are discussed from the perspective of today’s growing problem with online misinformation. vii Table of Contents List of Tables .......................................................................................................................x Chapter 1: Defining False News ..........................................................................................1 Chapter 2: Diffusion of Misinformation ..............................................................................5 Why People Believe Misinformation .........................................................................5 Correcting Misinformation Exposure .........................................................................8 Credibility Heuristics and False News......................................................................11 Heuristics for Credibility Assessment of Online News ............................................15 Chapter 3: Base-Rates and False News Detection .............................................................24 Deception Detection and Base-Rates ........................................................................24 Chapter 4: Method .............................................................................................................27 Stimulus False News Stories.....................................................................................27 Story Diffusion Feature Coding ................................................................................28 Social Consensus ..........................................................................................28 Moral-Emotional Language ..........................................................................28 Belief Congruence ........................................................................................29 Political Congruence .....................................................................................32 Control Variables ......................................................................................................33 Procedure ..................................................................................................................34 Chapter 5: Results ..............................................................................................................36 Diffusion of False News ...........................................................................................36 Participants ....................................................................................................36 Diffusion and News Features ........................................................................38 viii Detection of False News ...........................................................................................44 Participants ....................................................................................................44 Detection and News Features........................................................................47 Base-Rate Effect on Detection ......................................................................50 Chapter 6: Discussion ........................................................................................................53 Contributions to Misinformation Literature .............................................................56 Contributions to Truth-Default Theory.....................................................................60 Practical Contributions .............................................................................................63 Limitations ................................................................................................................64 Future Directions ......................................................................................................65 Appendices ........................................................................................................................68 Appendix A: Facebook Article Example ...........................................................................68 Appendix B: Facebook Mock Feed Example ....................................................................69 Appendix C: Meyer’s (1988) News Credibility Scale .......................................................70 Appendix D: Internet Scavenger Hunt ...............................................................................71 References .........................................................................................................................72 ix List of Tables Table 1. Belief Items ..........................................................................................................31 Table 2. Perceived News Credibility in Diffusion Study ....................................................37 Table 3. News Exposure by Medium in Diffusion Study ....................................................37 Table 4. Social Media Site Use in Diffusion Study ............................................................38 Table 5. Social Media Site Engagement in Diffusion Study ..............................................38 Table 6. Liking GEE Logistic Regression Coefficients ......................................................42 Table 7. Sharing GEE Logistic Regression Coefficients ...................................................43